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Abstract
Background: Addiction is a complex phenotype, though it consistently includes characteristics of impulsivity. A number of brain regions are suggested to be involved in cocaine addiction, including the insula, which serves diverse functions including interoceptive awareness and integration of neural signals from sensory, subcortical and frontal regions. Malfunction of this integration links impulsive behavior to the insula. Objectives: This study examines intrinsic connectivity of the insula in chronic cocaine users to investigate abnormal insular circuitry, its role in cocaine addiction, and relationships to measure of impulsivity. Methods: Cocaine-dependent individuals (n = 33) and healthy controls (n = 32) completed a resting-state fMRI scan. An intrinsic connectivity network (ICN) approach generated metrics of mean network connectivity and inter-network connectivity from fMRI data. Metrics pertaining to ICNs involving insula and other structures repeatedly involved in addiction (e.g. striatum) were selected for analysis, which included the capacity to discriminate groups. Relationships between group discriminating connectivity metrics and behavioral impulsivity were examined. Results: Models demonstrated group prediction accuracy up to 75%. Accuracy of 69% was obtained by a parsimonious model of six inter-network connectivity metrics. The inter-network connectivity between an ICN involving the anterior insula and ACC, and an ICN involving the striatum, was significantly weaker in cocaine users relative to controls. The degree of reduced inter-network connectivity was significantly related to greater non-planning impulsivity in cocaine users. Conclusions: Aberrant insula-derived intrinsic connectivity patterns are observed in cocaine users and include dysfunctions in insula to striatal connectivity, which is furthermore linked to increased impulsivity pertaining to forethought.
Acknowledgements
We are grateful to Andrew Poppe for optimizing and improving the code implemented during the analyses, and to Chris Bell for his time and efforts involved in the data collection. We are also grateful for the computing resources as well as technical support from the University of Minnesota Supercomputing Institute, and the neuroimaging resources and technical support from the Center for Magnetic Resonance Research at the University of Minnesota.